{"id":"W3000797342","doi":"10.1016/j.jneumeth.2020.108593","title":"A framework for quality control of corpus callosum segmentation in large-scale studies","year":2020,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo; NYU Langone Medical Center","keywords":"Segmentation; Pattern recognition (psychology); Artificial intelligence; Computer science; Support vector machine; Ground truth; Classifier (UML); Metric (unit); Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00121269,0.00006897794,0.0003794192,0.00007037183,0.0000380848,0.000005680227,0.000124438,0.00002596567,9.191309e-7],"category_scores_gemma":[0.004419724,0.00005385523,0.0001025155,0.0003727629,0.0001065262,0.00009518689,0.0000249915,0.0002236854,5.820187e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002565776,"about_ca_system_score_gemma":0.00005134685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.042724e-7,"about_ca_topic_score_gemma":1.908778e-7,"domain_scores_codex":[0.9988218,0.000173013,0.0005362065,0.0001479763,0.0001956084,0.0001253854],"domain_scores_gemma":[0.9983445,0.000758126,0.0004950269,0.0001096251,0.0002068914,0.0000858421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002414493,0.0001452865,0.00996591,0.00009215892,0.000004486885,0.000007209297,0.0005306698,0.0001104748,0.978723,0.001612172,0.00004858414,0.008518545],"study_design_scores_gemma":[0.008627434,0.006133451,0.1945277,0.0006560538,0.0003250281,0.0002625042,0.002456011,0.03338498,0.6567305,0.08093023,0.01554026,0.000425811],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06902089,0.000218131,0.9215936,0.008722548,0.0001184258,0.0003000862,0.000009668051,0.00001148737,0.000005170457],"genre_scores_gemma":[0.3034279,0.0001580837,0.6932619,0.00308286,0.00004644614,0.00001183985,1.233608e-7,0.000005913086,0.000004973051],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3219925,"threshold_uncertainty_score":0.5291142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3644151572613744,"score_gpt":0.5804615676928803,"score_spread":0.2160464104315059,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}